Baxt W G, Skora J
Department of Emergency Medicine, University of California, San Diego Medical Center, USA.
Lancet. 1996 Jan 6;347(8993):12-5. doi: 10.1016/s0140-6736(96)91555-x.
Artificial neural networks apply non-linear statistics to pattern recognition problems. One such problem is acute myocardial infarction (AMI), a diagnosis which, in a patient presenting as an emergency, can be difficult to confirm. We report here a prospective comparison of the diagnostic accuracy of a network and that of physicians, on the same patients with suspected AMI.
Emergency department physicians who evaluated 1070 patients 18 years or older presenting to the emergency department of a teaching hospital in California, USA with anterior chest pain indicated whether they thought these patients had sustained a myocardial infarction. The network analysed the patient data collected by the physicians during their evaluations and also generated a diagnosis.
The physicians had a diagnostic sensitivity and specificity for myocardial infarction of 73.3% (95% confidence interval 63.3-83.3%) and 81.1% (78.7-83.5%), respectively, while the network had a diagnostic sensitivity and specificity of 96.0% (91.2-100%) and 96.0% (94.8-97.2%), respectively. Only 7% of patients had had an AMI, a low frequency but typical for anterior chest pain.
The application of non-linear neural computational analysis via an artificial neural network to the clinical diagnosis of myocardial infarction appears to have significant potential.
人工神经网络将非线性统计方法应用于模式识别问题。急性心肌梗死（AMI）的诊断就是这样一个问题，对于一名急诊患者来说，很难确诊。我们在此报告对同一组疑似AMI患者，比较神经网络与医生诊断准确性的前瞻性研究。
在美国加利福尼亚州一家教学医院急诊科，评估了1070例18岁及以上因胸痛前来就诊的患者，急诊科医生指出他们认为这些患者是否发生了心肌梗死。神经网络分析了医生在评估过程中收集的患者数据，并也做出了诊断。
医生对心肌梗死的诊断敏感性和特异性分别为73.3%（95%置信区间63.3 - 83.3%）和81.1%（78.7 - 83.5%），而神经网络的诊断敏感性和特异性分别为96.0%（91.2 - 100%）和96.0%（94.8 - 97.2%）。只有7%的患者发生了AMI，发生率较低，但对于胸痛来说很典型。
通过人工神经网络将非线性神经计算分析应用于心肌梗死的临床诊断似乎具有很大潜力。
